A New Hybrid XGBSVM Model: Application for Hypertensive Heart Disease

被引：27

作者：

Chang, Wenbing ^{[1
]}

Liu, Yinglai ^{[1
]}

Wu, Xueyi ^{[2
,3
,4
]}

Xiao, Yiyong ^{[1
]}

Zhou, Shenghan ^{[1
]}

Cao, Wen ^{[5
]}

机构：

[1] Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100191, Peoples R China

[2] Fuwai Hosp, Hypertens Ctr, Beijing 100037, Peoples R China

[3] Chinese Acad Med Sci, Natl Ctr Cardiovasc Dis, Beijing 100050, Peoples R China

[4] Peking Union Med Coll, Beijing 100730, Peoples R China

[5] Peninsula Reg Med Ctr, Dept Operat Performance Improvement, Salisbury, MD 21801 USA

来源：

IEEE ACCESS | 2019年 / 7卷

基金：

中国国家自然科学基金;

关键词：

XGBSVM; hypertensive heart disease; SVM; XGBoost; biomedicine; RANDOM FOREST; PREDICTION; SVM;

D O I：

10.1109/ACCESS.2019.2957367

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The changes in people's life rhythm and improvement in material levels that happened in recent years increased the number of people suffering from high blood pressure in the world. Therefore, as a cardiac complication of hypertension, the prevalence of hypertensive heart disease has increased annually, it has seriously endangered the safety of human life, and the effective prediction of hypertensive heart disease has become a worldwide problem. This paper uses the newly proposed XGBSVM hybrid model to predict whether hypertensive patients will develop hypertensive heart disease within three years. The final experiment proves that through this model, hypertensive patients can learn their risk of hypertensive heart disease within 3 years and then undergo targeted preventive treatment, thereby reducing the psychological, physiological and economic burden. This paper confirms that the machine learning can be successfully applied in the biomedical field, with strong real-world significance and research value.

引用

页码：175248 / 175258

页数：11

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